No-reference image quality metric for NeRF (Neural Radiance Fields) rendering in automotive applications

Neural Radiance Fields (NeRF) rendering is a promising Artificial intelligence (AI) technology for generating photorealistic views, with significant potential for automotive applications. However, traditional metrics such as Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Learned Perceptual Image Patch Similarity (LPIPS) often fail to evaluate the model’s quality for novel viewpoints outside the dataset range, which is crucial for real-life use.